The theme of this presentation is architectures for Knowledge
Discovery in Databases and Data Mining (KDD) over enterprise
intranets. We outline our approach and describe our research
and development experiences in implementing and testing the
feasibilities in a corporate environment. Our initial implementation
effort (*), which is part of our concept of a highly available,
flexible workbench for a complete knowledge discovery process
for a wide variety of corporate data, focused on algorithms
for the semi-automated discovery of association rules. Some
of the challenges posed to KDD in an enterprise environment
include heterogeneity of computer and database systems; legacy
data; the physical distribution of various sources of data;
the necessity to provide advanced decision support for a diverse
set of users who will access, analyze, and discover over a gloabl
network using heterogeneous clients; a significant requirement
to reduce cost of systems and software; the need to provide
tools and techniques that will enable a user to perform all
the steps of the entire KDD process (ie., from raw data to acting
upon useful discovered knowledge); very large size databases;
ease of development; performance; flexibility; etc. These challenges
identify a new set of directions for KDD research and development
at both academia and industry.